Days in A/R by Practice Size: What Healthy Actually Looks Like

Two practices can report the same 38 days in accounts receivable and have very different revenue cycle problems.

The first is a small physician group with relatively straightforward claims, a concentrated payer mix and very little old A/R. Most claims move quickly, but one recurring front-end issue keeps the overall average higher than management would like. The second is a large multispecialty group. Its 38-day figure combines dozens of locations, multiple specialties, complicated claims, patient balances and different payer behaviors. Some service lines perform exceptionally well while others carry substantial balances beyond 90 or 120 days.

Calling both practices "38-day organizations" hides most of what management needs to know. Days in A/R is useful precisely because it compresses a complicated revenue cycle into a number. The danger begins when that number is treated as a complete diagnosis.

What Days in A/R Measures

Days in accounts receivable estimates how many days of revenue are represented by the organization's outstanding receivables. Different benchmarking frameworks use somewhat different definitions and inputs, so teams should know which version they are calculating.

For example, the Healthcare Financial Management Association defines net days in A/R as:

Net A/R ÷ average daily net patient service revenue

HFMA describes the metric as a trending indicator of overall A/R performance and revenue cycle efficiency. Practice-oriented calculations may use current receivables and average daily charges. MGMA materials describe days in A/R as the average number of days it takes a practice to be paid and provide a calculation based on current receivables, net of credits, divided by average daily charges.

That difference matters when comparing numbers. If one organization calculates gross days from charges while another uses net patient service revenue, their reported figures are not perfectly interchangeable. Even inside one organization, changing the calculation method can create an apparent performance change without any underlying operational improvement or deterioration.

Before asking whether your days in A/R are healthy, establish exactly what sits in the numerator and denominator.

Practice Size Changes the Context More Than the Formula

There is no mathematical rule under which a 10-provider group should have one healthy days-in-A/R number while a 500-provider group should automatically have another. Size affects the operating environment around the metric.

A smaller practice may have fewer handoffs between charge capture, billing, denial management and follow-up. The billing staff may know recurring payer issues from memory, and unusual claims may become visible quickly. That simplicity can be an advantage, although smaller teams can also be fragile. One vacancy, one experienced biller leaving, or one unresolved payer problem can consume a meaningful share of the organization's revenue cycle capacity.

Large groups have a different set of tradeoffs. They can build specialized teams for denials, payment posting, prior authorization and payer follow-up. They may have stronger analytics and more standardized processes. At the same time, they often manage more locations, providers, specialties and payer relationships, which creates additional handoffs and more opportunities for variation.

Scale can therefore improve revenue cycle performance in some respects while making aggregate metrics harder to interpret. A 40-day organization with 500 providers may contain one specialty operating at 25 days and another at 65. The enterprise average describes the portfolio but does not tell management where intervention belongs.

As organizations grow, segmentation becomes more important.

Specialty Mix Can Move the Number Without Reflecting Team Quality

Specialty is another reason a universal benchmark can mislead. Different practices generate different types of claims, documentation requirements, authorization dependencies, charge amounts and payer mixes. Their patient-responsibility profiles may differ as well.

Consider a primary care group and a multispecialty organization that includes procedural services. Even with equally capable billing teams, their claims may move through different administrative pathways.

MGMA explicitly recommends filtering benchmarking data carefully rather than assuming every practice is comparable. Its benchmarking guidance notes, for example, that hospital-owned and physician-owned practices should be separated in relevant comparisons because centralized services and organizational structures can affect reported operating data. The same reasoning should guide A/R analysis more broadly. Useful peer comparisons should be as close as practical on dimensions that materially affect the revenue cycle, including specialty and organizational structure. Payer mix, geography and service mix can add further context.

This is why broad statements such as "every practice should be below X days" deserve caution. A benchmark can tell you where to investigate. It cannot tell you, by itself, why your number differs.

The Aging Distribution Explains What the Average Hides

Suppose two practices both report 35 days in A/R. Practice A has most of its outstanding receivables concentrated in the youngest aging buckets, with relatively little lingering beyond 90 or 120 days. Practice B has a larger pool of very old receivables offset by a high volume of recently billed accounts.

The headline number looks identical. The collection problem does not. This is why days in A/R should almost always be read alongside aging buckets such as:

MGMA similarly recommends monitoring A/R aging alongside overall days in A/R, including the 0 to 30, 31 to 60, 61 to 90, 91 to 120 and 120-plus-day buckets. The distribution gives the average a shape. If days in A/R rises because recently billed volume increased substantially, the operational interpretation may differ from a rise driven by balances accumulating beyond 120 days. The same headline movement can therefore require different responses.

Diagnose the Movement Before Chasing the Benchmark

When days in A/R deteriorates, the first management question should be where the additional days came from. Start upstream.

Has charge lag increased? Are claims leaving the practice cleanly? Has a particular denial category grown? Are authorizations delaying certain claims? Has payer follow-up slowed? Are patient balances aging differently? Is one location or specialty responsible for a disproportionate share of the movement?

Then segment the metric. Break days in A/R and aging down by payer category, specialty, provider, location or another operational dimension that fits the organization. Averages become much more useful when the team can identify which component is changing. Finally, examine human effort. A practice can maintain respectable days in A/R while requiring an unsustainable amount of manual work to do it. MGMA highlighted this limitation in 2025, noting that traditional revenue cycle metrics such as days in A/R show outcomes but may fail to reveal how many staff touches were required to produce them.

That observation changes how a "healthy" metric should be interpreted. A revenue cycle that reaches 32 days through repeated calls, manual portal checks and extensive rework may have a very different operating profile from one reaching the same number with fewer interventions.

The outcome matters, and so does the process producing it.

What Healthy Looks Like for Your Practice

A benchmark becomes most useful when it is paired with internal trend data. Compare your organization with a relevant external peer group, but also compare the practice with itself. Look at whether days in A/R is improving or deteriorating over time, whether old balances are accumulating, and whether certain specialties or locations are moving differently from the organization overall.

Then connect the financial metric to the operating causes. A practice should be able to explain why the number is moving. If days in A/R increases by five days, leadership should be able to trace that movement to specific parts of the workflow rather than simply telling the billing team to collect faster.

For a small practice, that analysis may require only a few useful cuts of the data. For a large group, the enterprise average should become the starting point for segmentation by specialty, location, payer and aging. The size of the organization changes how much complexity sits beneath the metric, while the underlying management principle remains consistent: a benchmark tells you that something deserves attention, and the operating data tells you what to do about it.

Sources

• Healthcare Financial Management Association, MAP Keys: Industry-standard revenue cycle KPIs: https://www.hfma.org/data-and-insights/map-initiative/map-keys/
• Medical Group Management Association, Days in A/R: https://www.mgma.com/getkaiasset/64027d0a-cff9-43c6-8b43-17d6588d2413/PRCM-KPIWhitePaper-Final-19April23.pdf
• Medical Group Management Association, Understanding cost and revenue data: https://www.mgma.com/articles/understanding-cost-and-revenue-data
• Medical Group Management Association, Beyond days in A/R: Building RCM reporting around human effort, 2025: https://www.mgma.com/articles/beyond-days-in-ar-building-rcm-reporting-around-human-effort

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